Stroke Genetics Network (SiGN) Study

Meschia, James F.; Arnett, Donna K.; Ay, Hakan; Brown, Robert D.; Benavente, Oscar; Cole, John W.; Bakker, Paul I. W. de; Dichgans, Martin; Doheny, Kimberly F.; Fornage, Myriam; Grewal, Raji P.; Gwinn‐Hardy, Katrina; Jern, Christina; Conde, Jordi Jiménez; Johnson, Julie A.; Jood, Katarina; Laurie, Cathy C.; J, Lee; Lindgren, Arne; Markus, Hugh S.; McArdle, Patrick F.; McClure, Leslie A.; Mitchell, Braxton D.; Schmidt, Reinhold; Rexrode, Kathryn M.; Rich, Stephen S.; Rosand, Jonathan; Rothwell, Peter M.; Rundek, Tatjana; Sacco, Ralph L.; Sharma, Pankaj; Shuldiner, Alan R.; Słowik, Agnieszka; Wassertheil‐Smoller, Sylvia; Sudlow, Cathie; Thijs, Vincent; Woo, Daniel; Worrall, Bradford B.; Wu, Ona; Kittner, Steven J.

doi:10.1161/strokeaha.113.001857

Cited by 61 publications

(43 citation statements)

References 15 publications

(15 reference statements)

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“…This MR analysis was conducted with summary statistics from 2 large ischemic stroke genetics consortia: the METASTROKE Collaboration 3,8 and the National Institute of Neurologic Disorders and Stroke (NINDS)-Stroke Genetics Network (SiGN). 9 GWAS from these consortia contributed a total of 18,476 ischemic stroke cases and 37,296 controls of mainly European ancestry. Overlapping participants in the 2 consortia were removed.…”

Section: Resultsmentioning

confidence: 99%

“…Summary statistics for the association of each SNP with the risk factors and ischemic stroke as a whole and the 3 main subtypes (large artery, small vessel, and cardioembolic strokes) were acquired from previously published GWAS of the risk factors [13][14][15][16][17][18] and 2 stroke consortia (METASTROKE and NINDS-SiGN) , respectively. 8,9 The SNP-risk factor and SNPstroke associations were used to compute estimates of each risk factor-stroke association using an inverse-variance weighted method 19 (hereafter referred to as conventional MR analysis). We conducted complementary analyses using the weighted median and MR-Egger regression methods.…”

Section: Resultsmentioning

confidence: 99%

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Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes

Larsson¹,

Scott²,

Traylor³

et al. 2017

Neurology

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Objective: To implement a mendelian randomization (MR) approach to determine whether type 2 diabetes mellitus (T2D), fasting glucose, fasting insulin, and body mass index (BMI) are causally associated with specific ischemic stroke subtypes.Methods: MR estimates of the association between each possible risk factor and ischemic stroke subtypes were calculated with inverse-variance weighted (conventional) and weighted median approaches, and MR-Egger regression was used to explore pleiotropy. The number of single nucleotide polymorphisms (SNPs) used as instrumental variables was 49 for T2D, 36 for fasting glucose, 18 for fasting insulin, and 77 for BMI. Genome-wide association study data of SNPstroke associations were derived from METASTROKE and the Stroke Genetics Network (n 5 18,476 ischemic stroke cases and 37,296 controls).Results: Conventional MR analysis showed associations between genetically predicted T2D and large artery stroke (odds ratio [OR] 1.28, 95% confidence interval [CI] 1.16-1.40, p 5 3.3 3 10 27 ) and small vessel stroke (OR 1.21, 95% CI 1.10-1.33, p 5 8.9 3 10 25 ) but not cardioembolic stroke (OR 1.06, 95% CI 0.97-1.15, p 5 0.17). The association of T2D with large artery stroke but not small vessel stroke was consistent in a sensitivity analysis using the weighted median method, and there was no evidence of pleiotropy. Genetically predicted fasting glucose and fasting insulin levels and BMI were not statistically significantly associated with any ischemic stroke subtype. Conclusions:This study provides support that T2D may be causally associated with large artery stroke. Neurology ® 2017;89:454-460 GLOSSARY BMI 5 body mass index; CI 5 confidence interval; GWAS 5 genome-wide association studies; MR 5 mendelian randomization; NINDS 5 National Institute of Neurologic Disorders and Stroke; 1KG 5 1000 Genomes; OR 5 odds ratio; SiGN 5 Stroke Genetics Network; SNP 5 single nucleotide polymorphism; TOAST 5 Trial of Org 10172 in Acute Stroke Treatment; T2D 5 type 2 diabetes mellitus.Stroke is one of the leading causes of disability and death worldwide.1 The burden of stroke is projected to increase considerably in the next decades. Hence, there is a need to develop effective prevention strategies for stroke, which necessitates a better understanding of the underlying risk factors. About 80% of all strokes are ischemic, 1 but this term describes a syndrome caused by a number of different pathologies, which may have different treatments.2 The major etiologies of ischemic stroke are large artery atherosclerosis (large artery stroke), small vessel atherosclerosis (small vessel stroke), and cardioembolism (cardioembolic stroke). Recent data from genomewide association studies (GWAS) have shown that these subtypes have distinctive risk factor profiles and, by implication, diverse pathophysiologic bases. Type 2 diabetes mellitus (T2D) is associated with an increased risk of ischemic stroke, 4 but its relative contribution to different ischemic stroke subtypes is unknown. Moreover, several risk factors t...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes

Larsson¹,

Scott²,

Traylor³

et al. 2017

Neurology

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“…Further large GWASs of stroke, including the National Institute of Neurological Disorders and Stroke SiGN study, which will contribute another 10,000 stroke cases, are nearing completion. 59 Accurate classification of subtypes The subtype specificity of associations identified to date highlights the importance of accurate phenotyping with a system that classifies according to pathophysiology. The fact that subtype-specific associations have been found by using the TOAST classification system is reassuring, but improved subtyping classifications may increase the yield further.…”

Section: The Future Of Gwassmentioning

confidence: 99%

Mechanisms and treatment of ischaemic stroke—insights from genetic associations

Markus

Bevan

2014

Nat Rev Neurol

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| The precise pathophysiology of ischaemic stroke is unclear, and a greater understanding of the different mechanisms that underlie large-artery, cardioembolic and lacunar ischaemic stroke subtypes would enable the development of more-effective, subtype-specific therapies. Genome-wide association studies (GWASs) are identifying novel genetic variants that associate with the risk of stroke. These associations provide insight into the pathophysiological mechanisms, and present opportunities for novel therapeutic approaches. In this Review, we summarize the genetic variants that have been linked to ischaemic stroke in GWASs to date and discuss the implications of these associations for both our understanding and treatment of ischaemic stroke. The majority of genetic variants identified are associated with specific subtypes of ischaemic stroke, implying that these subtypes have distinct genetic architectures and pathophysiological mechanisms. The findings from the GWASs highlight the need to consider whether therapies should be subtype-specific. Further GWASs that include large cohorts are likely to provide further insights, and emerging technologies will complement and build on the GWAS findings.

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“…In this scenario, it has been pointed out that well-established stroke biobanks with close collaboration between clinicians and geneticists are essential for supplying the biological and clinical information required for these large-scale studies 4,5,6 . To address these challenges specifically, recent cooperative efforts have been launched such as the Stroke Genetics Network and Bio-Repository of DNA in Stroke 7,8 . As defined by Brazilian regulatory guidelines, a biobank represents an organized collection of human biological material and associated data that are prospectively collected and stored for research purposes, in accordance with predefined technical, ethical and operational standards, under institutional responsibility and management.…”

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confidence: 99%

Joinville stroke biobank: study protocol and first year’s results

Ferreira¹,

França²,

Nagel³

et al. 2017

Arq. Neuro-Psiquiatr.

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Aiming to contribute to studies that use detailed clinical and genomic information of biobanks, we present the initial results of the first Latin American Stroke Biobank. Methods: Blood samples were collected from patients included in the Joinville Stroke Registry and four Brazilian cities. Demographic socio-economic data, cardiovascular risk factors, Causative Classification System for Ischemic Stroke, Trial of Org 10172 in Acute Stroke Treatment and National Institutes of Health scores, functional stroke status (modified Rankin) and brain images were recorded. Additionally, controls from both geographic regions were recruited. High-molecular-weight genomic DNA was obtained from all participants. Results: A total of 2,688 patients and 3,282 controls were included. Among the patients, 76% had ischemic stroke, 12% transient ischemic attacks, 9% hemorrhagic stroke and 3% subarachnoid hemorrhage. Patients with undetermined ischemic stroke were most common according the Trial of Org 10172 in Acute Stroke Treatment (40%) and Causative Classification System for Ischemic Stroke (47%) criteria. A quarter of the patients were under 55 years of age at the first-ever episode. Conclusions: We established the Joinville Stroke Biobank and discuss its potential for contributing to the understanding of the risk factors leading to stroke.

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Stroke Genetics Network (SiGN) Study

Cited by 61 publications

References 15 publications

Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes

Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes

Mechanisms and treatment of ischaemic stroke—insights from genetic associations

Joinville stroke biobank: study protocol and first year’s results

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